Data-driven e-business environments have to deal with overwhelmingly large amounts of data. These large amounts of data can make it difficult to answer the “Why?” questions when analyzing the customer experience. Why overall business today is different from business yesterday, why it is different from business the same day last week, why a particular segment is doing better or worse compared to the same segment same week a month ago? Knowing the answer to these questions is crucial to online business management. One of the widely accepted approaches to finding the answers to the “why” questions is based on segmentation of customer experience.
Customer segmentation refers to identifying events associated with different groups of users. These groups can be composed or revealed based for example, on customer location, demography, patterns of the customer experience, etc. The more sophisticated segmentation methods combine several criteria together to produce a set of business-relevant segments. Performing customer segmentation by statistical methods is notoriously difficult. Customer segmentation is usually performed empirically based on previous similar experience (customers that bought product A usually also buy product B) or heuristic speculations (Asian customers usually prefer product C). Some customer segmentation methods use cluster analysis and decision trees. One hurdle to successful application of these methods is determining how to properly classify the different user groups.